{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salary in First Tier Cities 一线城市工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "developers_in_24_cities=conn.execute('select count(1) from _201904 where monthly_salary>0 and monthly_salary<15').fetchall()[0][0]\n",
    "data_original=pd.read_sql(sql=\"select * from _201904 where (city_beijing=1 or city_shanghai=1 or city_guangzhou=1 or city_shenzhen=1) and monthly_salary>0 and monthly_salary<15\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']\n",
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除其他城市\n",
    "for col in data.columns:\n",
    "    if col.startswith('city_') and not col in ['city_beijing','city_shanghai','city_guangzhou','city_shenzhen']:\n",
    "        del data[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_category_column(data, prefix):\n",
    "    prefix_=prefix+\"_\"\n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    data.loc[:,prefix]=['']*data.shape[0]\n",
    "    for feature in features:\n",
    "        data.loc[data[feature]==1,prefix]=feature.replace(prefix_,'')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:362: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "data=add_category_column(data, 'city')\n",
    "data=add_category_column(data, 'career')\n",
    "data=add_category_column(data, 'edu')\n",
    "data=add_category_column(data, 'experience')\n",
    "data=add_category_column(data, 'company_type')\n",
    "data=add_category_column(data, 'company_size')\n",
    "data=add_category_column(data, 'industry')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "139573 Jobs found in all the 24 major cities in China. 77536 or 55.55% of them are in the first tier cities.\n"
     ]
    }
   ],
   "source": [
    "print('{} Jobs found in all the 24 major cities in China. {} or {:.2%} of them are in the first tier cities.'.\n",
    "      format(developers_in_24_cities, data.shape[0], data.shape[0]*1.0/developers_in_24_cities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在24个主要城市中，共找到139573个职位，其中77536（55.55%）属于一线城市。\n"
     ]
    }
   ],
   "source": [
    "print('在24个主要城市中，共找到{}个职位，其中{}（{:.2%}）属于一线城市。'.\n",
    "      format(developers_in_24_cities, data.shape[0], data.shape[0]*1.0/developers_in_24_cities))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019年中国大陆一线城市程序员的平均工资为15716元，工资中位数为14000元，其中95%的人的工资位于5250到36500元之间。\n"
     ]
    }
   ],
   "source": [
    "salary_mean=data.monthly_salary.mean()*10000\n",
    "salary_median=data.monthly_salary.median()*10000\n",
    "q=np.quantile(data.monthly_salary,[0.025,0.975])*10000\n",
    "print('2019年中国大陆一线城市程序员的平均工资为{:.0f}元，工资中位数为{:.0f}元，其中95%的人的工资位于{:.0f}到{:.0f}元之间。'\n",
    "      .format(salary_mean, salary_median, q[0], q[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2019, Developers in the first tier cities earn 15716 Yuan as average, the median is 14000 Yuan, 95% of them earn between 5250 and 36500 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print('In 2019, Developers in the first tier cities earn {:.0f} Yuan as average, '\n",
    "      'the median is {:.0f} Yuan, 95% of them earn between {:.0f} and {:.0f} Yuan.'\n",
    "      .format(salary_mean, salary_median, q[0], q[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b77d3848d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=42343.17993165874, pvalue=0.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<4].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(0.5, 500), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_category_column(data, prefix):\n",
    "    prefix_=prefix+\"_\"\n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    data.loc[:,prefix]=['']*data.shape[0]\n",
    "    for feature in features:\n",
    "        data.loc[data[feature]==1,prefix]=feature.replace(prefix_,'')\n",
    "    #data[prefix]=new_column\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    count=[]\n",
    "    for feature in features:\n",
    "        salary_mean.append(data[data[feature]==1].monthly_salary.mean()*10000)\n",
    "        salary_median.append(data[data[feature]==1].monthly_salary.median()*10000)\n",
    "        count.append(data[data[feature]==1].shape[0])\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "beijing      1.745311\n",
       "guangzhou    1.303173\n",
       "shanghai     1.662637\n",
       "shenzhen     1.563134\n",
       "Name: monthly_salary, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['city']).monthly_salary.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       career           \n",
       "beijing    algorithm            2.447160\n",
       "           architect            2.647334\n",
       "           software_engineer    1.576653\n",
       "           spider               1.720098\n",
       "guangzhou  algorithm            1.790865\n",
       "           architect            1.992640\n",
       "           software_engineer    1.227792\n",
       "           spider               1.295122\n",
       "shanghai   algorithm            2.260754\n",
       "           architect            2.487171\n",
       "           software_engineer    1.550493\n",
       "           spider               1.617029\n",
       "shenzhen   algorithm            2.130034\n",
       "           architect            2.454403\n",
       "           software_engineer    1.460794\n",
       "           spider               1.367124\n",
       "Name: monthly_salary, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['city','career']).monthly_salary.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       career           \n",
       "beijing    algorithm            2.250000\n",
       "           architect            2.500000\n",
       "           software_engineer    1.400000\n",
       "           spider               1.675000\n",
       "guangzhou  algorithm            1.600000\n",
       "           architect            2.000000\n",
       "           software_engineer    1.150000\n",
       "           spider               1.250000\n",
       "shanghai   algorithm            2.083333\n",
       "           architect            2.300000\n",
       "           software_engineer    1.458333\n",
       "           spider               1.500000\n",
       "shenzhen   algorithm            2.000000\n",
       "           architect            2.250000\n",
       "           software_engineer    1.250000\n",
       "           spider               1.250000\n",
       "Name: monthly_salary, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['city','career']).monthly_salary.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       career           \n",
       "beijing    algorithm             1771\n",
       "           architect              733\n",
       "           software_engineer    11285\n",
       "           spider                  34\n",
       "guangzhou  algorithm             1135\n",
       "           architect              843\n",
       "           software_engineer    15049\n",
       "           spider                  41\n",
       "shanghai   algorithm             2360\n",
       "           architect             1305\n",
       "           software_engineer    22154\n",
       "           spider                  69\n",
       "shenzhen   algorithm             1805\n",
       "           architect              927\n",
       "           software_engineer    17974\n",
       "           spider                  51\n",
       "Name: monthly_salary, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['city','career']).monthly_salary.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_ff44a2da_582f_11e9_a048_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >career_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ff44a2da_582f_11e9_a048_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow0_col0\" class=\"data row0 col0\" >architect</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow0_col1\" class=\"data row0 col1\" >24003.4</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow0_col2\" class=\"data row0 col2\" >22500</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow0_col3\" class=\"data row0 col3\" >3810</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow0_col4\" class=\"data row0 col4\" >4.78%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ff44a2da_582f_11e9_a048_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >0</th> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow1_col0\" class=\"data row1 col0\" >algorithm</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow1_col1\" class=\"data row1 col1\" >23149.6</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow1_col2\" class=\"data row1 col2\" >22500</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow1_col3\" class=\"data row1 col3\" >9306</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow1_col4\" class=\"data row1 col4\" >11.67%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ff44a2da_582f_11e9_a048_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow2_col0\" class=\"data row2 col0\" >software_engineer</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow2_col1\" class=\"data row2 col1\" >14575.9</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow2_col3\" class=\"data row2 col3\" >66605</td> \n",
       "        <td id=\"T_ff44a2da_582f_11e9_a048_701ce71031efrow2_col4\" class=\"data row2 col4\" >83.55%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22da2d1c208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career=get_sub_stats(data,'career_')\n",
    "data_career.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有表可知，架构师的工资平均为2.1万，算法工程师为2万，普通程序员为1万4。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=10318.190675760517, pvalue=0.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "career_architect_salary=data[data.career_architect==1].monthly_salary\n",
    "career_algorithm_salary=data[data.career_algorithm==1].monthly_salary\n",
    "career_software_engineer_salary=data[data.career_software_engineer==1].monthly_salary\n",
    "stats.f_oneway(career_algorithm_salary, career_software_engineer_salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=15.425156918591176, pvalue=8.627749154400873e-05)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(career_architect_salary, career_algorithm_salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "统计表面，架构师，算法工程师，普通程序员之间的平均工资差异是显著的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_fffb98b6_582f_11e9_b335_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >6</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow0_col0\" class=\"data row0 col0\" >haskell</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow0_col1\" class=\"data row0 col1\" >25666.7</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow0_col2\" class=\"data row0 col2\" >26250</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow0_col3\" class=\"data row0 col3\" >10</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >9</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow1_col0\" class=\"data row1 col0\" >julia</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow1_col1\" class=\"data row1 col1\" >23750</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow1_col2\" class=\"data row1 col2\" >23750</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow1_col3\" class=\"data row1 col3\" >2</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow1_col4\" class=\"data row1 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >18</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow2_col0\" class=\"data row2 col0\" >rust</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow2_col1\" class=\"data row2 col1\" >22746.5</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow2_col2\" class=\"data row2 col2\" >20000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow2_col3\" class=\"data row2 col3\" >95</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow2_col4\" class=\"data row2 col4\" >0.09%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >19</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow3_col0\" class=\"data row3 col0\" >scala</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow3_col1\" class=\"data row3 col1\" >22622</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow3_col2\" class=\"data row3 col2\" >20833.3</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow3_col3\" class=\"data row3 col3\" >917</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow3_col4\" class=\"data row3 col4\" >0.88%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >5</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow4_col0\" class=\"data row4 col0\" >groovy</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow4_col1\" class=\"data row4 col1\" >20411.7</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow4_col2\" class=\"data row4 col2\" >17500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow4_col3\" class=\"data row4 col3\" >50</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow4_col4\" class=\"data row4 col4\" >0.05%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >12</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow5_col0\" class=\"data row5 col0\" >matlab</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow5_col1\" class=\"data row5 col1\" >19937.8</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow5_col2\" class=\"data row5 col2\" >17500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow5_col3\" class=\"data row5 col3\" >1522</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow5_col4\" class=\"data row5 col4\" >1.46%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >16</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow6_col0\" class=\"data row6 col0\" >python</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow6_col1\" class=\"data row6 col1\" >19708.6</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow6_col2\" class=\"data row6 col2\" >17500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow6_col3\" class=\"data row6 col3\" >9355</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow6_col4\" class=\"data row6 col4\" >8.97%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >4</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow7_col0\" class=\"data row7 col0\" >go</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow7_col1\" class=\"data row7 col1\" >19388.2</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow7_col2\" class=\"data row7 col2\" >17500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow7_col3\" class=\"data row7 col3\" >8298</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow7_col4\" class=\"data row7 col4\" >7.96%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >25</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow8_col0\" class=\"data row8 col0\" >r</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow8_col1\" class=\"data row8 col1\" >19366</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow8_col2\" class=\"data row8 col2\" >17500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow8_col3\" class=\"data row8 col3\" >400</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow8_col4\" class=\"data row8 col4\" >0.38%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >14</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow9_col0\" class=\"data row9 col0\" >perl</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow9_col1\" class=\"data row9 col1\" >18748.3</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow9_col2\" class=\"data row9 col2\" >17500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow9_col3\" class=\"data row9 col3\" >920</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow9_col4\" class=\"data row9 col4\" >0.88%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >2</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow10_col0\" class=\"data row10 col0\" >cpp</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow10_col1\" class=\"data row10 col1\" >17745.9</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow10_col2\" class=\"data row10 col2\" >15000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow10_col3\" class=\"data row10 col3\" >17154</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow10_col4\" class=\"data row10 col4\" >16.45%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row11\" class=\"row_heading level0 row11\" >17</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow11_col0\" class=\"data row11 col0\" >ruby</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow11_col1\" class=\"data row11 col1\" >17577.4</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow11_col2\" class=\"data row11 col2\" >17500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow11_col3\" class=\"data row11 col3\" >404</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow11_col4\" class=\"data row11 col4\" >0.39%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row12\" class=\"row_heading level0 row12\" >11</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow12_col0\" class=\"data row12 col0\" >lua</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow12_col1\" class=\"data row12 col1\" >17079.8</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow12_col2\" class=\"data row12 col2\" >15000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow12_col3\" class=\"data row12 col3\" >1609</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow12_col4\" class=\"data row12 col4\" >1.54%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row13\" class=\"row_heading level0 row13\" >10</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow13_col0\" class=\"data row13 col0\" >kotlin</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow13_col1\" class=\"data row13 col1\" >16869.5</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow13_col2\" class=\"data row13 col2\" >15000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow13_col3\" class=\"data row13 col3\" >264</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow13_col4\" class=\"data row13 col4\" >0.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row14\" class=\"row_heading level0 row14\" >7</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow14_col0\" class=\"data row14 col0\" >java</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow14_col1\" class=\"data row14 col1\" >16267.5</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow14_col2\" class=\"data row14 col2\" >15000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow14_col3\" class=\"data row14 col3\" >29060</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow14_col4\" class=\"data row14 col4\" >27.87%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row15\" class=\"row_heading level0 row15\" >0</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow15_col0\" class=\"data row15 col0\" >assembly</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow15_col1\" class=\"data row15 col1\" >16140.1</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow15_col2\" class=\"data row15 col2\" >15000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow15_col3\" class=\"data row15 col3\" >699</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow15_col4\" class=\"data row15 col4\" >0.67%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row16\" class=\"row_heading level0 row16\" >20</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow16_col0\" class=\"data row16 col0\" >swift</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow16_col1\" class=\"data row16 col1\" >16111.8</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow16_col2\" class=\"data row16 col2\" >14000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow16_col3\" class=\"data row16 col3\" >948</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow16_col4\" class=\"data row16 col4\" >0.91%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row17\" class=\"row_heading level0 row17\" >21</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow17_col0\" class=\"data row17 col0\" >typescript</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow17_col1\" class=\"data row17 col1\" >15181.2</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow17_col2\" class=\"data row17 col2\" >14000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow17_col3\" class=\"data row17 col3\" >678</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow17_col4\" class=\"data row17 col4\" >0.65%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row18\" class=\"row_heading level0 row18\" >13</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow18_col0\" class=\"data row18 col0\" >objective_c</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow18_col1\" class=\"data row18 col1\" >14783.1</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow18_col2\" class=\"data row18 col2\" >12500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow18_col3\" class=\"data row18 col3\" >146</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow18_col4\" class=\"data row18 col4\" >0.14%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row19\" class=\"row_heading level0 row19\" >15</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow19_col0\" class=\"data row19 col0\" >php</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow19_col1\" class=\"data row19 col1\" >14779.4</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow19_col2\" class=\"data row19 col2\" >12500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow19_col3\" class=\"data row19 col3\" >5587</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow19_col4\" class=\"data row19 col4\" >5.36%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row20\" class=\"row_heading level0 row20\" >8</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow20_col0\" class=\"data row20 col0\" >javascript</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow20_col1\" class=\"data row20 col1\" >13840.9</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow20_col2\" class=\"data row20 col2\" >12500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow20_col3\" class=\"data row20 col3\" >15164</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow20_col4\" class=\"data row20 col4\" >14.54%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row21\" class=\"row_heading level0 row21\" >1</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow21_col0\" class=\"data row21 col0\" >c_sharp</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow21_col1\" class=\"data row21 col1\" >13463.5</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow21_col2\" class=\"data row21 col2\" >12500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow21_col3\" class=\"data row21 col3\" >10332</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow21_col4\" class=\"data row21 col4\" >9.91%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row22\" class=\"row_heading level0 row22\" >24</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow22_col0\" class=\"data row22 col0\" >visual_basic_net</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow22_col1\" class=\"data row22 col1\" >13031.9</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow22_col2\" class=\"data row22 col2\" >12500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow22_col3\" class=\"data row22 col3\" >268</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow22_col4\" class=\"data row22 col4\" >0.26%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row23\" class=\"row_heading level0 row23\" >22</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow23_col0\" class=\"data row23 col0\" >vba</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow23_col1\" class=\"data row23 col1\" >13028.5</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow23_col2\" class=\"data row23 col2\" >12500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow23_col3\" class=\"data row23 col3\" >114</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow23_col4\" class=\"data row23 col4\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row24\" class=\"row_heading level0 row24\" >3</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow24_col0\" class=\"data row24 col0\" >delphi</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow24_col1\" class=\"data row24 col1\" >11855.2</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow24_col2\" class=\"data row24 col2\" >11500</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow24_col3\" class=\"data row24 col3\" >254</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow24_col4\" class=\"data row24 col4\" >0.24%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fffb98b6_582f_11e9_b335_701ce71031eflevel0_row25\" class=\"row_heading level0 row25\" >23</th> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow25_col0\" class=\"data row25 col0\" >visual_basic</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow25_col1\" class=\"data row25 col1\" >11652.8</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow25_col2\" class=\"data row25 col2\" >12000</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow25_col3\" class=\"data row25 col3\" >18</td> \n",
       "        <td id=\"T_fffb98b6_582f_11e9_b335_701ce71031efrow25_col4\" class=\"data row25 col4\" >0.02%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22db141bb38>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats(data,'pl_')\n",
    "data_pl.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，haskell才是最赚钱的编程语言。python是主流语言里面最赚钱的，比java的工资多了3000元！vb是最不赚钱的了。其中，最赚钱的编程语言和最不赚钱的，工资居然差了2倍。所以，要选好编程语言呀！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_00910c7e_5830_11e9_90e2_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >edu_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_00910c7e_5830_11e9_90e2_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >5</th> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow0_col0\" class=\"data row0 col0\" >phd</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow0_col1\" class=\"data row0 col1\" >30308</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow0_col2\" class=\"data row0 col2\" >29000</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow0_col3\" class=\"data row0 col3\" >112</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.17%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_00910c7e_5830_11e9_90e2_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >3</th> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow1_col0\" class=\"data row1 col0\" >master</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow1_col1\" class=\"data row1 col1\" >23084.4</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow1_col2\" class=\"data row1 col2\" >21000</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow1_col3\" class=\"data row1 col3\" >2051</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow1_col4\" class=\"data row1 col4\" >3.13%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_00910c7e_5830_11e9_90e2_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >1</th> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow2_col0\" class=\"data row2 col0\" >bachelor</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow2_col1\" class=\"data row2 col1\" >16840.8</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow2_col2\" class=\"data row2 col2\" >15000</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow2_col3\" class=\"data row2 col3\" >42268</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow2_col4\" class=\"data row2 col4\" >64.50%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_00910c7e_5830_11e9_90e2_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >0</th> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow3_col0\" class=\"data row3 col0\" >associate</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow3_col1\" class=\"data row3 col1\" >12828.3</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow3_col2\" class=\"data row3 col2\" >12500</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow3_col3\" class=\"data row3 col3\" >20879</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow3_col4\" class=\"data row3 col4\" >31.86%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_00910c7e_5830_11e9_90e2_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow4_col0\" class=\"data row4 col0\" >middle_school</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow4_col1\" class=\"data row4 col1\" >11798.1</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow4_col3\" class=\"data row4 col3\" >26</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow4_col4\" class=\"data row4 col4\" >0.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_00910c7e_5830_11e9_90e2_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >2</th> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow5_col0\" class=\"data row5 col0\" >high_school</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow5_col1\" class=\"data row5 col1\" >9580.51</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow5_col2\" class=\"data row5 col2\" >7000</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow5_col3\" class=\"data row5 col3\" >195</td> \n",
       "        <td id=\"T_00910c7e_5830_11e9_90e2_701ce71031efrow5_col4\" class=\"data row5 col4\" >0.30%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22da1b5eb00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats(data,'edu_')\n",
    "data_edu.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大专，本科，硕士，博士的平均工资分别是12828，16840，23084，30308。中位数分别是12500， 15000，21000， 29000。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_phd=data[data.edu_phd==1].monthly_salary\n",
    "salary_master=data[data.edu_master==1].monthly_salary\n",
    "salary_bachelor=data[data.edu_bachelor==1].monthly_salary\n",
    "salary_associate=data[data.edu_associate==1].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=37.99934598382358, pvalue=8.419586730682308e-10)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1039.3610697025629, pvalue=2.0403673390552008e-225)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=3866.5985931587775, pvalue=0.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%watermark` not found.\n"
     ]
    }
   ],
   "source": [
    "%watermark --iversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'edu')\n",
    "order=['phd','master','bachelor','associate']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_0317bfb0_5830_11e9_a607_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >experience_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_0317bfb0_5830_11e9_a607_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow0_col0\" class=\"data row0 col0\" >10</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow0_col1\" class=\"data row0 col1\" >32645.6</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow0_col2\" class=\"data row0 col2\" >30000</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow0_col3\" class=\"data row0 col3\" >305</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.39%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0317bfb0_5830_11e9_a607_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >3</th> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow1_col1\" class=\"data row1 col1\" >22569.4</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow1_col2\" class=\"data row1 col2\" >20500</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow1_col3\" class=\"data row1 col3\" >11100</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow1_col4\" class=\"data row1 col4\" >14.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0317bfb0_5830_11e9_a607_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow2_col1\" class=\"data row2 col1\" >16587.6</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow2_col2\" class=\"data row2 col2\" >15000</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow2_col3\" class=\"data row2 col3\" >23940</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow2_col4\" class=\"data row2 col4\" >30.88%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0317bfb0_5830_11e9_a607_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >4</th> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow3_col0\" class=\"data row3 col0\" >no</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow3_col1\" class=\"data row3 col1\" >14244.6</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow3_col2\" class=\"data row3 col2\" >12500</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow3_col3\" class=\"data row3 col3\" >18327</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow3_col4\" class=\"data row3 col4\" >23.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0317bfb0_5830_11e9_a607_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >1</th> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow4_col1\" class=\"data row4 col1\" >12567.8</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow4_col2\" class=\"data row4 col2\" >11500</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow4_col3\" class=\"data row4 col3\" >23864</td> \n",
       "        <td id=\"T_0317bfb0_5830_11e9_a607_701ce71031efrow4_col4\" class=\"data row4 col4\" >30.78%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22db14d9470>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats(data,'experience_')\n",
    "data_experience.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience_10==1].monthly_salary * 10000\n",
    "salary_we_5_10=data[data.experience_5_10==1].monthly_salary * 10000\n",
    "salary_we_3_5=data[data.experience_3_5==1].monthly_salary * 10000\n",
    "salary_we_1_3=data[data.experience_1_3==1].monthly_salary * 10000\n",
    "salary_we_no=data[data.experience_no==1].monthly_salary * 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3p1dPfWcJwxhjjNORRoGIPAO8COzzFarqC3GJyhhjTFJymjQOBfbgub25jwKWNIwx5iDi9JLbP8Q7EGOMMckvrk/uM8YY07g4PTz1MPBX4F/geXKfiDwJzIpXYMbjq11N/M/P8PmyrAlAtXKn7XWNSWTGmIOR06TRUlVXVXl8ZrQn95k6CnfrjbTiYgBadOxY4za7RmjXGGOiUtWoP3i+Cf4r4EPv9Dgg38m89f3Tt29fbci++OIL7d69e7Xyxx9/XLds2eKfvueee3T37t0x63fEiBFaXFysqqpPPvmkzpo1K2L9/v37++NdsGBBUJxXXXVVjfp2u9363HPP1TDi2Ln11lv1iSee0AcffFDz8vLq1NaWLVv0d7/7narWbl3EWmZmpu7bt09zcnL0hx9+CFsv3HZnDh5AgTr4jHX6PY2r8Bya8j2571og/EOVTczNnTuXrVu3+qdnz57Nnj17YtL23r17+f777+nQoQMAr732GiNHjow4z7vvvgtAUVERTz75ZEziSJQlS5YwfPhw/vSnP3HxxRfXqa1f/vKXPP/88zGKrG6Kioro0KEDzZo1Y/HixRx++OGJDokDBw4kOgRTR46ShqpuVtWhwJFAN1U9VVWL4hrZQezAgQOMHz+e7t27M3z4cObPn09BQQEXXHABvXv35t5772Xr1q0MHjyYwYMHA5Cens5f/vIXMjMzGTJkCNu3bwc8z+Q+8cQTycjI4LzzzgvZ35tvvsmgQYMAz8hz7dq1ZGZmsn37doYNG0ZmZiZXXHEFxx57LCUlJf7+AG688UbefvttevfuzT333APA1q1bGTlyJF26dOH666/39+ObB+D555/nkksu8U+//vrrDBgwgK5du/Lqq6+GjHPevHlkZGTQq1cvLrroIgC+/PJLhgwZQkZGBkOGDOGrr74C4JJLLuHKK69k8ODBdOrUibfeeos//vGPnHDCCUH9/vjjj+zfv58jjzyS3Nxc/v73vwMwaNAgfM+aLykpweVyATBq1CjWrVsHQJ8+fZgxYwYAU6ZM4ZFHHqGoqIgePXpUi33RokX079+fkpISLrnkkqDE4lsvu3btYsiQIWRmZtKzZ09eeuklwPPhf8IJJwRtE3v37gVg9erVZGRk0L9/f/76178G9Z2fn+9P/i6Xy//e3X333fTo0YMePXowe/Zsf/2KigrcbjcZGRmMGzcu4k7JjTfe6N+urrvuOv86D7Vcb775JoMHD+b888+nZ8+e3HDDDfzjH//w18vNzeWuu+4C4M477+Skk04iIyODadOm+dftvffe669/yy232LPmEynSMAS40Pt7UqgfJ0OZ+v5pDIenmjRpoh999JGqqp599tk6f/58HThwoK5evdpf79hjj9Xt27f7pwF94oknVFV1+vTp/sMi7du3159++klV1X94YvXq1XrppZf6550wYYIuW7ZMVVXXrFmjF110kaqqXnXVVXrrrbeqqmp+fr4C/j7T0tJUVXX58uU6evRof1uPP/64Hnfccbpjxw7du3evHnPMMfrVV18FzaOq+txzz6nb7VZVz+GpESNG6IEDB7SwsFA7dOige/fuDVovn376qXbt2tXff2lpqaqqjhkzRufOnauqqo8++qieeeaZ/jbPPfdcrays1BdffFFbtWql69at0wMHDmhmZqZ//f773//WKVOmqKrqtGnT9M4771RVDVrf27dv12OPPVZVVW+77Ta9//77defOnZqVlaXDhw9XVdVBgwbphg0bgg7z+A5PvfDCC3rqqafq999/748t8HCcb72Ul5frzp07/X3+6le/0srKyrDbhKpq9+7d9Z133lFV1RtuuCHoENMZZ5yhn3/+uar+vL0UFBRojx49dNeuXVpWVqYnnniifvjhh/rFF18ooCtXrlRV1T/84Q/+dVFVaWmpdu3aVSsrK1X15+0q3HItX75cW7ZsqZs3b1ZV1Q8//FCzs7P99U444QT98ssv9T//+Y+OHz9eKysr9cCBAzp69Gh966239IsvvtA+ffqoquqBAwe0U6dOWlJSEjI2U3vE6PBUmvd3qzA/Jg6OO+44evfuDUDfvn0pKiqKOk9KSgrnnnsuABdeeCErV64EICMjgwsuuIAnnniC1FTPdQ9ZWVk88sgj/nnfeecdTj31VMBzaConJweAlStX+kcnI0eOpHXr1o7iHzJkCIcddhgtWrTgxBNP5Msvv4w6zznnnENKSgpdunShU6dObNiwIejvb7zxBuPGjaNt27YAHHHEEQC89957nH/++QBcdNFF/uUGOP300xERevbsSbt27ejZsycpKSl0797dv04Dl9eJAQMGsGLFClauXMno0aPZtWsXe/bsoaioiOOPP75a/eXLl/O3v/2NRYsWRV1/qsrNN99MRkYGQ4cOZcuWLXz33XdA6G1ix44dlJWVccoppwD41wPA/v37KS4uplOnTkF9rFy5krFjx5KWlkZ6ejpnnXUWb7/9NgBHH300v/nNb4DgbaiqQw89lBYtWnDZZZfxwgsv0LJly6jrrV+/fhx33HGAZ4S2bds2tm7dyscff0zr1q055phjWLJkCUuWLKFPnz5kZmayYcMGNm7ciMvlok2bNnz00Uf+v7dp0yZqnyY+Il49par/EpEmwI+qek89xXTQa97854ciNmnSxH8ooiZ8V7otWrSIFStW8PLLLzNz5kzWr1/vTx4Amzdv5uijj6ZZs2aA5/j+v//9bwDfaLPO8VdUVATFBPDTTz+FjDfctKpWKwslsI4vjpSUlKCYUlJS/DGtWrWKBx98sFo7qampVFZWVov1pJNOoqCggE6dOjFs2DBKSkp4+OGH6du3b8h4OnXqxObNmyksLCQrK6ta26rK/v37AViwYAHbt29nzZo1NG3aFJfL5e871DYR6f15++23/TsCgSLNE+098ElNTWXVqlUsW7aMp59+mvvvv5833ngj7HIBpKWlBbUxbtw4nn/+eb799lv/jomqctNNN3HFFVdU6/Oyyy5j7ty5fPvtt/zxj38Muwwm/qKe01DVA8AZ9RCLiaBVq1aUlZWFna6srPQfT37yySc59dRTqays5Ouvv2bw4MHccccd7Nixg127dgW1G3jce+fOnVRUVPj34k499VSeffZZwJNMfvjhh6hxRdKuXTs+++wzKisrWbhwYdDfnnvuOSorK/n888/ZvHlztb32IUOG8Oyzz1JaWgrA999/D8App5zC008/DXg+dEN9UIazfv16unXrRpMmTar9zeVysWbNGoCg4/TNmjXj6KOP5tlnn+Xkk09mwIAB/P3vf2fAgAEh+zj22GN54YUXuPjii1m/fn21tl966SXKy8sBz/o/6qijaNq0KcuXL486QmvdujWtWrXi/fffB/CvBwg/gsrOzubFF19kz5497N69m4ULF/pj/+qrr3jvvfcAeOqpp8Kuy127drFz505GjRrF7NmzWbt2bcTlCuW8887j6aef5vnnn2fcuHEAjBgxgscee8y/jW7ZsoVt27YBMHbsWF577TVWr17NiBEjIq4XE19Or556V0TuF5EBIpLp+4lrZCbIJZdcwp/+9Cd69+7N3r17ufzyy8nJyfGfCE9LS2P9+vX07duXN954g6lTp3LgwAEuvPBCevbsSZ8+ffjzn//M4YcfTkFBAZdddhkQfKXU0qVLGTp0qL/PadOmsWTJEjIzM8nPz6d9+/a0ahV8VDIjI4PU1FR69erlPxEezu23386YMWM47bTTaN++fdDfjj/+eAYOHEhOTg7//Oc/adGiBVu3bmXUqFEAdO/enVtuuYWBAwfSq1cvJk2aBHhO9D/++ONkZGQwf/78oBOm0QQmTB/f3vV1113Hgw8+yCmnnOI/gewzYMAA2rVrR8uWLRkwYADFxcVhk4Zv2RYsWMDZZ5/N559/zvjx43nrrbfo168fH3zwgX8v/IILLqCgoICsrCwWLFhAt27doi7Do48+yuWXX07//v1RVQ477DDAc/J54MCB1ZYtMzOTSy65hH79+vHrX/+ayy67jD59+gBwwgknkJeXR0ZGBt9//z1XXhn6AsmysjLGjBlDRkYGAwcO9L/v4ZYrlO7du1NWVkaHDh3828Lw4cM5//zz6d+/Pz179mTcuHH+HZJmzZoxePBgzjnnnJBJ3tQjJyc+gOUhft5wMm99/zT0E+G1FXiS2amffvpJA9fXpZdequ+9917Q38vLy1VV9d1339VevXrVPdAkMnToUN26dat/+uqrr9bHHnssgRHVXFlZmf/1bbfdphMnTtSvv/5aR44c6S+vqKjQI444Qvfv35+IEGPiwIED2qtXLy0sLEx0KI0WDk+EO71h4eB4Ja2DzZw5c9i0aVNQWbH3G94do3zDu3PnzjF91nbz5s39l5UCQSfHwXO44pxzzqGyspJmzZrx8MMPx6zvZLB06VL/6ylTpvDBBx+Qm5ubuIBqYdGiRdx2221UVFRw7LHHMnfuXI488kjy839+MnP37t257LLLaNq0aQIjrb3//ve/jBkzhrFjx9KlS5dEh3PQE3VwslNE2gG3Ar9U1RwRORHor6qPxjvAmsrKytLAD8JkM3HiRD5a/xEEfs9qh/d3pO9e7YA+3fvY9emmXo0dO5YvvvgiqOxvf/ubnVdohERkjapmRavn9N5Tc4HHgVu804XAM0DSJY0G4XCoHFTpn0x503NqKbCsKl8dY+pT1QsWjHH6SdRWVZ8FKgFUtQKw+wEYY8xBxmnS2C0ibfA8rQ8RORnYGbeojDHGJCWnh6cmAS8DvxKRd/Dcg2pc3KIyxhiTlJxePfWhiAwEjgcE+D9VDf/NHWOMMY2So6QhIi2A/wFOxXOI6m0R+aeq/hR5TmOMMY2J08NT84Ay4D7v9O+B+cDZ8QjKGGNMcnKaNI5X1V4B08tF5ON4BGSMMSZ5Ob166iPvFVMAiMivgXfiE1JymTNnTqP8Ql1jXS5jTHw5HWn8GrhYRL7yTh8DfCYinwCqqhlxiS4JVL3lR2PRWJfLGBNfTpNG5AdGG2OMOSg4TRpdVPX1wAIRcatqXhxiMsYYk6ScntOYKiIPikiaiLQTkVeA0+MZmDHGmOTjNGkMBD4H1gIrgSdV1b4RbowxBxmnSaM1npPhnwP7gGPFyQObjTHGNCpOk8b7QL6qjgROAn7JQXLJrTHGmJ85PRE+FBgoIlNVdYaI/B1wxS+s+vf6668zY8aMkH/r3bt3PUdTf7Kzsx3Xbd26Nbt372b//v3+smOOOYaSkhL27NnjL1uxYgXz58+v9qS/5s2bs3//fk477TSWLVuG2+3m0ksvDRtLdnY2Z5xxBtdffz0ul4u77rqLNm3aAHDFFVfw2Wef0bNnTx544AEAbrzxRt59912ys7OZNWsWhYWFXHPNNRx55JEUFRXRrVs3mjdvTm5urr+dkpISpk+f7i8bN24c27Zto3379jzzzDMA/nbuu+8+Onfu7I9v9OjRlJWVcdhhh/HKK69EXHdV+wknXF8muTl9fxtDDE5HGjcBJ+O5fQh4bilyV1wiSpBbb7010SEkvR9++CEoYYDnkbCBCcMn1KNh9+3bh6qybNkyAPLyIl98t2LFCnJzc6msrGTz5s1B9T/77DMAPvnkE3/Zu+++658PYNasWezevZuioiIANmzYwLp164LaycvLCyrbtm0bAN98842/jq+dqjsVZWVlAOzcGf0pAVX7CSdcXya5OX1/G0MMTpPGr1X1KuAnAFX9AWhWmw5FpIWIrBKRj0VkvYhMj1B3roh8ISJrvT9x2eV//fXXqaioCPv3tWvXxqPbhIvXctVk9PLoo49GnGfXrl3+16+++iqlpaVcccUVQXWuuuoqbrzxxqCya6+91p8sAqkq+fn5lJaWUlJSQn5+vr9s7NixQXXPPfdcCgsL/e0UFRX5vxQ5evTooLqnnx7+YsKq/ZSWloasF64vk9ycvr+NJQanh6fKRaQJPz+E6Ui8T/GrhX3Aaaq6S0SaAitFJF9V3w9T/6+q+nwt+3LEyShj4sSJMelr48aNtVtzuzzzxjSOJJCXlxd0iCqSiooK8vLy/KMMn8DRhs+HH34Ytp3Kykry8vJQVVTVX1b1H+2bb75h1qxZQWUzZsxg3rx5/lGGT6TRhq+vwL7PXqADAAASN0lEQVQnTZpUrV64vkxyc/r+NpYYnI405gALgaNE5H/xXHZbq+M56uHbfWzq/dHatOUjIpeLSIGIFGzfvr3G80caZZjksmTJkjq3UV5ezpIlS1i6dCnl5eX+slCqjlZCjV6iqdpPuGWIRV+m/jl9fxtLDE4fwrRARNYAQ/A8hOm3qvpZlNnC8o5a1gCdgQdU9YMI1f9XRKYCy4AbVXVfiPgeAh4CyMrKqnECSk1NjZo4YnVzv4kTJ/LRlo9qPmM6dOnQJaZxNMTDbsOHD+fFF1+sUxtNmzZl+PDhqCqLFy+mvLycpk2bhkwcLpcr6MPb5XLVuL9hw4YF9TN8+PCQ9WLRl6l/Tt/fxhKD05EGqrpBVR9Q1fvrkjC8bR1Q1d5AR6CfiPQIU/UmoBuey3yPAG6oS7/h3HzzzfFo1jjgdrsd101NTcXtdnPCCScElffs2ZNTTjklqCwzMzNsOykpKbjdbtxuN76vG6WkpFS74qR9+/ZMnjw5qGzq1KkAtGrVKqj8sMMOC9tf1X7CLXO4vkxyc/r+NpYYHCeNeFDVHcCbhLkhoqp+4z2ctQ94HOgXjziGDh1Kamr4QVdjveQ2Xsvlu3rJCd/5jHDzpKen+1+PGTOGNm3a8K9//SuozgMPPMDtt98eVDZ79uyQe+oiQk5ODm3atKFt27bk5OT4yxYuXBhU95lnnqFr167+dlwul/8y2EWLFgXVjXTJbdV+wl0OGa4vk9ycvr+NJYZ6TxoicqSIHO59fQie74BsCFO3vfe3AL8FPo1XXDbaiK5169Y0axZ80dwxxxxDy5Ytq9UdP358tbLmzZsjIgwZMgSIPsrIzs4mNzeXlJQUOnXqFFTfN9ro2bOnv8w32vBdiTV58mTS0tL8H8TdunUjIyMjqB232x1UdtRRRwGeUYaPr52qe/6+0UakUUa4fsIJ15dJbk7f38YQg/jOuNcXEckA8oAmeJLWs6oa8qJ0EXkDOBLPeZS1wJ8CTqKHlJWVpQUFBTGL13e1UqzPaVQO+vkSqpQ3Pbk7sKyqlDdT6NOhT0zjgNgtlzGmYRORNaqaFa2e00tuY0ZV1wF9HNY9Lc7hGGOMqYGEntMwxhjTsNT7SCMUEVkIHFel+AZV/U8i4jHGGBNaUiQNVR0bvZYxxphEs8NTxhhjHEuKkUYya6zXyjfW5TLGxJcljShidYPAZNNYl8sYE192eMoYY4xjljSMMcY4ZknDGGOMY5Y0jDHGOGZJwxhjjGOWNIwxxjhmScMYY4xjljSMMcY4Zl/uS4QdPz9DwzcNVcpCzEOHuEZljDFRWdKoZ6Fu31GsxQB07NAx/Iwd7NYfxpjEs6RRz+z2HcaYhszOaRhjjHHMkoYxxhjHLGkYY4xxzJKGMcYYxyxpGGOMccyShjHGGMcsaRhjjHHMkoYxxhjH7Mt9MTZnzhw2bdoUVFZc7P3Gd8fw3/ju3LmzffHPGJP0LGnE2KZNm9iwdi2/CCgr8/7eUVIScp5v4x6VMcbEhiWNOPgFcCnin34UhSplgXx/N8aYZGfnNIwxxjhmScMYY4xjljSMMcY4ZknDGGOMY5Y0jDHGOGZJwxhjjGOWNIwxxjhmScMYY4xjljS85syZw5w5cxIdRkw0pmUxxiQX+0a4V9X7RTVkjWlZjDHJxUYaxhhjHLOkYYwxxjFLGsYYYxyzpGGMMcYxSxrGGGMcs6RhjDHGMUsajdD27dtZu3Yt2dnZEX8mTJgQsnzYsGEhy++8885q8yxfvhwgqCzQpEmTyM7O5pprrmHChAmUlpZSUlLifz1//nyys7N56qmnGD16NNnZ2YwYMYJBgwaxZs2asMs4bdo0srOzmTlzpr+ssLCQnJwc/yXHvn4eeughfx/RVG0jsJ3S0tIavQ+BnLYRi76S1apVq6K+r/EWr/Ubarupb/UVQ0KShog8JiLbROTTKPVmisg6EVkrIktE5Jf1FWNDtmXLFkf11q1bF7J83759IctfeeWVavMEfmiHUlBQAMBHH33EunXryMvLIy8vz//64YcfBuDBBx+krMzzYNy9e/dSWVnJlClTwrbrS1ZLly71l82aNYvdu3czY8YMAH8/TzzxhL+PaKq2EdhOXl5e1PnDcdpGLPpKVrm5uVHf13iL1/oNtd3Ut/qKIVEjjbnASAf17lTVDFXtDbwKTI1rVI3AwoULHddVrfljZqvOU1FRUW104ZueNGlStXkXL17M4sWLUVVefvnliH3t2rUr5F7ptGnTgqZnzpxJYWEhRUVFABQVFVFQUEB+fn61eCONNqq2sWnTJkpKSvzt5Ofn12oP1WkbsegrWa1atYpdu3YB4d/XeIvX+g213dS3+oxBavPBEZOORVzAq6raw2H9m4BjVPXKSPWysrLUt3dbE2eddRZ79+6lS5cuNZ430MaNG0nZvZvra/CM8DtQKtPS6tw3wNq1a+vcRiysWLGiWjIBEPGsA6fbXXp6OosXLw4qC9Wuy+Xy/9P45tu3bx/l5eUhYwvl4osvDmrD5XLRq1cvFi9eTHl5OU2bNmX06NHVkmE0d911l6M2nNZriEaNGuVPGhD6fY23eK3fUNvNvHnz6txufccgImtUNStavaQ/pyEi/ysiXwMXEGakISKXi0iBiBRs3769fgM0NaKqNRrhBH7QRBL4D+ObL1TCqEkbRUVFLF261N9OeXk5S5YsqVGbgOM2YtFXsqr6Pjp9X2MpXus31HZT3+ozhqS/95Sq3gLc4h1pXA1MC1HnIeAh8Iw0atNPx44dAep8o7+JEyeyo4Z7+22Aw7t0iclNBgcOHFirw071pTYjDSdqMtJw2kaokcbw4cMdt+czbNgwR204rdcQpaenVxtp1Ld4rd9Q2019q88Ykn6kEeBJ4HeJDiLZXXvttYkOwS8rq/pIt2nTpqSmevZVUlKib36hTrQPHjw4aHrYsGFMnjw5qGzGjBn+BBXoyivDH92s2sbUqVNxu93+dlJSUnC73VFjrsppG7HoK1nl5uYGTUe7gCIe4rV+Q2039a0+Y0jqpCEigQf5zwA2JCqWhmLs2LGO64b6UK3pPKmpqdXOEfim77777mrzjho1ilGjRiEinHHGGRH7Sk9Pp2/fvtXKp0+fHjQ9ZcoUunbt6t+7crlcZGVlkZOTUy3e3//+92H7q9pG586dadu2rb+dnJwc2rRpEzHmUJy2EYu+klW/fv38o4tw72u8xWv9htpu6lt9xpCoS26fAt4DjheRYhG5NEzV20XkUxFZBwwHrqm3IBuwDh06OKqXkZERsrx58+Yhy08//fRq80S7fNI32ujTpw8ZGRm43W7cbrf/9fjx4wHPCKBVq1YAHHLIIaSkpETcG/WNNoYNG+Yvmzx5Mmlpaf69LF8/F154ob+PaKq2EdhOXfZMnbYRi76SVW5ubtT3Nd7itX5DbTf1rb5iSNjVU/FS26unJk6cCMTunMalNbh66lGUw3v3jtmDk2K1LMaYg0ejuXrKGGNM8kiKq6dE5AHgN1WK71XVxxMRjzHGmNCSImmo6lWJjsEYY0x0dnjKGGOMY5Y0jDHGOJYUh6eSQSKurY6XxrQsxpjkYknDy3eZamPQmJbFGJNc7PCUMcYYxyxpGGOMccyShjHGGMcsaRhjjHHMkoYxxhjHLGkYY4xxzJKGMcYYx+x7GnHwLT/fDh3gG+/vwLKq9Q+Pe1TGGFN3ljRiLNS3sXcVFwNwuPc55FUdHmY+Y4xJNpY0Ysy+jW2MaczsnIYxxhjHLGkYY4xxzJKGMcYYxyxpGGOMcUxUQ18G2lCJyHbgyxrO1hYoiUM48WLxxldDirchxQoWb7zVJd5jVfXIaJUaXdKoDREpUNWsRMfhlMUbXw0p3oYUK1i88VYf8drhKWOMMY5Z0jDGGOOYJQ2PhxIdQA1ZvPHVkOJtSLGCxRtvcY/XzmkYY4xxzEYaxhhjHDvok4aIjBSR/xORTSJyY6LjiUREjhaR5SLymYisF5FrEh1TNCLSREQ+EpFXEx1LNCJyuIg8LyIbvOu4f6JjikRE/uzdDj4VkadEpEWiYwokIo+JyDYR+TSg7AgRWSoiG72/WycyxkBh4r3Tuz2sE5GFIpI0N6QOFW/A364TERWRtrHu96BOGiLSBHgAyAFOBH4vIicmNqqIKoC/qOoJwMnAVUkeL8A1wGeJDsKhe4HXVLUb0IskjltEOgATgSxV7QE0Ac5LbFTVzAVGVim7EVimql2AZd7pZDGX6vEuBXqoagZQCNxU30FFMJfq8SIiRwPDgK/i0elBnTSAfsAmVd2sqvuBp4EzExxTWKr6jap+6H1dhudDrUNiowpPRDoCo4FHEh1LNCJyKJANPAqgqvtVdUdio4oqFThERFKBlsDWBMcTRFVXAN9XKT4TyPO+zgN+W69BRRAqXlVdoqoV3sn3gdDPN0iAMOsX4B7gegjzAJ86OtiTRgfg64DpYpL4QziQiLiAPsAHiY0kotl4Nt7KRAfiQCdgO/C493DaIyKSluigwlHVLcDf8exNfgPsVNUliY3KkXaq+g14doKAoxIcT038EchPdBCRiMgZwBZV/ThefRzsSUNClCX95WQikg78G7hWVX9MdDyhiMgYYJuqrkl0LA6lApnAg6raB9hNch06CeI9F3AmcBzwSyBNRC5MbFSNl4jcgufw8IJExxKOiLQEbgGmxrOfgz1pFANHB0x3JMmG+FWJSFM8CWOBqr6Q6Hgi+A1whogU4Tnsd5qIPJHYkCIqBopV1Tdyex5PEklWQ4EvVHW7qpYDLwCnJDgmJ74TkfYA3t/bEhxPVCLiBsYAF2hyf0fhV3h2Ij72/t91BD4UkV/EspODPWmsBrqIyHEi0gzPicSXExxTWCIieI65f6aqdyc6nkhU9SZV7aiqLjzr9Q1VTdo9YVX9FvhaRI73Fg0B/pvAkKL5CjhZRFp6t4shJPGJ+wAvA27vazfwUgJjiUpERgI3AGeo6p5ExxOJqn6iqkepqsv7f1cMZHq37Zg5qJOG9wTX1cB/8PzDPauq6xMbVUS/AS7Cs9e+1vszKtFBNSITgAUisg7oDdya4HjC8o6Ingc+BD7B87+cVN9eFpGngPeA40WkWEQuBW4HhonIRjxX+NyeyBgDhYn3fqAVsNT7//bPhAYZIEy88e83uUdbxhhjkslBPdIwxhhTM5Y0jDHGOGZJwxhjjGOWNIwxxjhmScMYY4xjljSMMcY4ZknDGIe8t07/n4DpQeFu+S4ib4pIVpzjmSsi4+LZhzFVWdIwxrnDgf+JWitJee+Ga0ydWNIwjZKIuLwPz3nE+5CiBSIyVETe8T4AqJ/3gUAveh+w876IZHjnzfU+4OZNEdksIhO9zd4O/Mr7zeA7vWXpAQ9uWuC9pUdgHJeKyD0B0+NFJOQtYEQkTUQWicjH3pjP9ZZPFZHV3rKHqvYRqY53GW4VkbeAW0TkC+/9yxCRQ0WkyDdtjBOWNExj1hnPg5UygG7A+cCpwHXAzcB04CPvA3ZuBuYFzNsNGIHnmSvTvB+sNwKfq2pvVf2rt14f4Fo8D/HqhOdWL4GexnPjRt8H8x+Ax8PEOxLYqqq9vA9Wes1bfr+qnuQtOwTPzfOqilTncFUdqKrTgTfxPOMEPPcE+7f3hofGOGJJwzRmX3hv4lYJrMfzxDjFc68mF54EMh9AVd8A2ojIYd55F6nqPlUtwXMn1nZh+lilqsXePtZ62/VT1d3AG8AYEekGNFXVT8K09QkwVET+JiIDVHWnt3ywiHwgIp8ApwHdQ8wbqc4zAa8fwZO4IHICMyYkSxqmMdsX8LoyYLoSz/MzIj1PJXDeA9760foIV+8R4BKifEiraiHQF0/yuM17yKkF8A9gnKr2BB4Ggp4F7qDO7oA+3gFcIjIQaKKq1Z4vbUwkljTMwWwFcAF4roQCSqI81KoMzx1Pa8R7R9qj8RweeypcPRH5JbBHVZ/A81S+TH7+8C/xPnwr1NVSTuoEmueNw0YZpsbsagpzMMvF83jXdcAefn7OQ0iqWuo9kf4pnsd+LqpBX88CvVX1hwh1egJ3ikglUA5cqao7RORhPKOPIjzPgKkaV9Q6VSwAZhEhgRkTjt0a3Zh64P0+xz2quiwJYhkHnKmqFyU6FtPw2EjDmDgSkcOBVcDHSZIw7gNyAHt4l6kVG2kYU89EpA0QKoEMUdXS+o7HmJqwpGGMMcYxu3rKGGOMY5Y0jDHGOGZJwxhjjGOWNIwxxjhmScMYY4xj/w8J6sXsCfzN4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'experience')\n",
    "order=['10','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，工资会随着工作经验的增长而增长。不过，我感觉那些招十年工作经验的，基本上都是架构师等职位。如果你的工作经验是一年的工作经验重复十次，应该拿不到那么高的工资。这个以后我还会仔细分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市 City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >city_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow0_col0\" class=\"data row0 col0\" >beijing</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow0_col1\" class=\"data row0 col1\" >17453.1</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow0_col2\" class=\"data row0 col2\" >15000</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow0_col3\" class=\"data row0 col3\" >13823</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow0_col4\" class=\"data row0 col4\" >17.83%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >2</th> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow1_col0\" class=\"data row1 col0\" >shanghai</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow1_col1\" class=\"data row1 col1\" >16626.4</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow1_col2\" class=\"data row1 col2\" >15000</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow1_col3\" class=\"data row1 col3\" >25888</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow1_col4\" class=\"data row1 col4\" >33.39%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >3</th> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow2_col0\" class=\"data row2 col0\" >shenzhen</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow2_col1\" class=\"data row2 col1\" >15631.3</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow2_col2\" class=\"data row2 col2\" >14000</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow2_col3\" class=\"data row2 col3\" >20757</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow2_col4\" class=\"data row2 col4\" >26.77%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >1</th> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow3_col0\" class=\"data row3 col0\" >guangzhou</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow3_col1\" class=\"data row3 col1\" >13031.7</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow3_col2\" class=\"data row3 col2\" >12000</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow3_col3\" class=\"data row3 col3\" >17068</td> \n",
       "        <td id=\"T_04a5dc70_5830_11e9_a1bb_701ce71031efrow3_col4\" class=\"data row3 col4\" >22.01%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22db141b908>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_city=get_sub_stats(data,'city_')\n",
    "data_city.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "程序员收入最高的城市分别为：北京，上海，深圳，广州"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_beijing=data[data.city_beijing==1].monthly_salary\n",
    "salary_shanghai=data[data.city_shanghai==1].monthly_salary\n",
    "salary_guangzhou=data[data.city_guangzhou==1].monthly_salary\n",
    "salary_shenzhen=data[data.city_shenzhen==1].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'city')\n",
    "order=['shenzhen','guangzhou','shanghai','beijing']\n",
    "order=['beijing','shanghai','guangzhou','shenzhen']\n",
    "\n",
    "sns.boxplot(y='city',x='monthly_salary',order=order,data=data[data.city.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(1.2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=80.32434741890425, pvalue=3.3121643665903536e-19)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_beijing, salary_shanghai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=2290.0597614426306, pvalue=0.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shanghai, salary_guangzhou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1191.135261747661, pvalue=5.103034674562747e-257)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_guangzhou, salary_shenzhen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "统计（Oneway Anova）显示，任何两个城市之间的平均工资差异都是显著的。\n",
    "\n",
    "The average salary between the four cities are significant different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_06604d5c_5830_11e9_835c_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >city_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_06604d5c_5830_11e9_835c_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >2</th> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow0_col0\" class=\"data row0 col0\" >shanghai</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow0_col1\" class=\"data row0 col1\" >16626.4</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow0_col2\" class=\"data row0 col2\" >15000</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow0_col3\" class=\"data row0 col3\" >25888</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow0_col4\" class=\"data row0 col4\" >33.39%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_06604d5c_5830_11e9_835c_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >3</th> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow1_col0\" class=\"data row1 col0\" >shenzhen</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow1_col1\" class=\"data row1 col1\" >15631.3</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow1_col2\" class=\"data row1 col2\" >14000</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow1_col3\" class=\"data row1 col3\" >20757</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow1_col4\" class=\"data row1 col4\" >26.77%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_06604d5c_5830_11e9_835c_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >1</th> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow2_col0\" class=\"data row2 col0\" >guangzhou</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow2_col1\" class=\"data row2 col1\" >13031.7</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow2_col2\" class=\"data row2 col2\" >12000</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow2_col3\" class=\"data row2 col3\" >17068</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow2_col4\" class=\"data row2 col4\" >22.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_06604d5c_5830_11e9_835c_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >0</th> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow3_col0\" class=\"data row3 col0\" >beijing</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow3_col1\" class=\"data row3 col1\" >17453.1</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow3_col2\" class=\"data row3 col2\" >15000</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow3_col3\" class=\"data row3 col3\" >13823</td> \n",
       "        <td id=\"T_06604d5c_5830_11e9_835c_701ce71031efrow3_col4\" class=\"data row3 col4\" >17.83%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22da628e0f0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_city.sort_values(by='head_count', ascending=False).style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data_city.city_, data_city.head_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_07574292_5830_11e9_855b_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >company_type_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >9</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow0_col0\" class=\"data row0 col0\" >us_eu</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow0_col1\" class=\"data row0 col1\" >18301.1</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow0_col2\" class=\"data row0 col2\" >17500</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow0_col3\" class=\"data row0 col3\" >3531</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow0_col4\" class=\"data row0 col4\" >4.96%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >7</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow1_col0\" class=\"data row1 col0\" >startup</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow1_col1\" class=\"data row1 col1\" >16353.1</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow1_col2\" class=\"data row1 col2\" >15000</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow1_col3\" class=\"data row1 col3\" >1777</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow1_col4\" class=\"data row1 col4\" >2.50%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >3</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow2_col0\" class=\"data row2 col0\" >listed</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow2_col1\" class=\"data row2 col1\" >16278.8</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow2_col2\" class=\"data row2 col2\" >15000</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow2_col3\" class=\"data row2 col3\" >7197</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow2_col4\" class=\"data row2 col4\" >10.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >2</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow3_col0\" class=\"data row3 col0\" >foreign_rep</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow3_col1\" class=\"data row3 col1\" >15714.3</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow3_col2\" class=\"data row3 col2\" >12500</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow3_col3\" class=\"data row3 col3\" >56</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow3_col4\" class=\"data row3 col4\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >8</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow4_col0\" class=\"data row4 col0\" >state</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow4_col1\" class=\"data row4 col1\" >15500.8</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow4_col2\" class=\"data row4 col2\" >14000</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow4_col3\" class=\"data row4 col3\" >3795</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow4_col4\" class=\"data row4 col4\" >5.33%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow5_col0\" class=\"data row5 col0\" >private</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow5_col1\" class=\"data row5 col1\" >15451.8</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow5_col2\" class=\"data row5 col2\" >13000</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow5_col3\" class=\"data row5 col3\" >50380</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow5_col4\" class=\"data row5 col4\" >70.81%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >0</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow6_col0\" class=\"data row6 col0\" >foreign</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow6_col1\" class=\"data row6 col1\" >15226.2</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow6_col2\" class=\"data row6 col2\" >12500</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow6_col3\" class=\"data row6 col3\" >3895</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow6_col4\" class=\"data row6 col4\" >5.47%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >6</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow7_col0\" class=\"data row7 col0\" >public_institution</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow7_col1\" class=\"data row7 col1\" >14149.4</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow7_col2\" class=\"data row7 col2\" >12500</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow7_col3\" class=\"data row7 col3\" >436</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow7_col4\" class=\"data row7 col4\" >0.61%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >4</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow8_col0\" class=\"data row8 col0\" >non_profit</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow8_col1\" class=\"data row8 col1\" >11548.8</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow8_col2\" class=\"data row8 col2\" >10500</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow8_col3\" class=\"data row8 col3\" >70</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow8_col4\" class=\"data row8 col4\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_07574292_5830_11e9_855b_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >1</th> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow9_col0\" class=\"data row9 col0\" >foreign_gov</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow9_col1\" class=\"data row9 col1\" >10527.8</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow9_col2\" class=\"data row9 col2\" >7000</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow9_col3\" class=\"data row9 col3\" >9</td> \n",
       "        <td id=\"T_07574292_5830_11e9_855b_701ce71031efrow9_col4\" class=\"data row9 col4\" >0.01%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22da855a6a0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats(data,'company_type_')\n",
    "data_company_type.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_us_eu=data[data.company_type_us_eu==1].monthly_salary\n",
    "salary_startup=data[data.company_type_startup==1].monthly_salary\n",
    "salary_listed=data[data.company_type_listed==1].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=53.96465474051579, pvalue=2.351111467004394e-13)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_us_eu,salary_startup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=146.1796365323301, pvalue=1.9559438664827014e-33)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_us_eu,salary_listed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上报可知，欧美外企的工资显著高于其他企业。比创业公司和上司公司足足高了2000元呀。要努力学好英语呀！\n",
    "\n",
    "顺便做个广告，大家如果想通过看美剧或者看英文书提高英语，可以上我的网站，美剧单词网。里面会把你不懂的单词列出来，你再去听美剧，听英语有声书（网站提供），就能听懂了。\n",
    "\n",
    "https://www.tvshowwords.com/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_08a16912_5830_11e9_9e82_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >company_size_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_08a16912_5830_11e9_9e82_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow0_col1\" class=\"data row0 col1\" >21269.5</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow0_col2\" class=\"data row0 col2\" >17500</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow0_col3\" class=\"data row0 col3\" >3676</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow0_col4\" class=\"data row0 col4\" >4.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_08a16912_5830_11e9_9e82_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow1_col0\" class=\"data row1 col0\" >1000_5000</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow1_col1\" class=\"data row1 col1\" >16960</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow1_col2\" class=\"data row1 col2\" >15000</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow1_col3\" class=\"data row1 col3\" >10858</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow1_col4\" class=\"data row1 col4\" >14.06%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_08a16912_5830_11e9_9e82_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >4</th> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow2_col0\" class=\"data row2 col0\" >5000_10000</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow2_col1\" class=\"data row2 col1\" >16854.4</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow2_col2\" class=\"data row2 col2\" >15000</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow2_col3\" class=\"data row2 col3\" >1784</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow2_col4\" class=\"data row2 col4\" >2.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_08a16912_5830_11e9_9e82_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >5</th> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow3_col0\" class=\"data row3 col0\" >500_1000</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow3_col1\" class=\"data row3 col1\" >16762.5</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow3_col2\" class=\"data row3 col2\" >15000</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow3_col3\" class=\"data row3 col3\" >8274</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow3_col4\" class=\"data row3 col4\" >10.71%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_08a16912_5830_11e9_9e82_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >2</th> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow4_col0\" class=\"data row4 col0\" >150_500</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow4_col1\" class=\"data row4 col1\" >15714.7</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow4_col2\" class=\"data row4 col2\" >14000</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow4_col3\" class=\"data row4 col3\" >17839</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow4_col4\" class=\"data row4 col4\" >23.09%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_08a16912_5830_11e9_9e82_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >6</th> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow5_col0\" class=\"data row5 col0\" >50_150</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow5_col1\" class=\"data row5 col1\" >14682.2</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow5_col2\" class=\"data row5 col2\" >12500</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow5_col3\" class=\"data row5 col3\" >22547</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow5_col4\" class=\"data row5 col4\" >29.19%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_08a16912_5830_11e9_9e82_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >3</th> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow6_col0\" class=\"data row6 col0\" >50</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow6_col1\" class=\"data row6 col1\" >13900.6</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow6_col2\" class=\"data row6 col2\" >12500</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow6_col3\" class=\"data row6 col3\" >12268</td> \n",
       "        <td id=\"T_08a16912_5830_11e9_9e82_701ce71031efrow6_col4\" class=\"data row6 col4\" >15.88%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22da8550f28>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats(data,'company_size_')\n",
    "data_company_size.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_0941e5c2_5830_11e9_a12d_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >industry_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >4</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow0_col0\" class=\"data row0 col0\" >finance</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow0_col1\" class=\"data row0 col1\" >17488.9</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow0_col2\" class=\"data row0 col2\" >16000</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow0_col3\" class=\"data row0 col3\" >3766</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow0_col4\" class=\"data row0 col4\" >3.99%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >2</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow1_col0\" class=\"data row1 col0\" >edu</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow1_col1\" class=\"data row1 col1\" >17159.8</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow1_col2\" class=\"data row1 col2\" >15000</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow1_col3\" class=\"data row1 col3\" >5812</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow1_col4\" class=\"data row1 col4\" >6.16%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >9</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow2_col0\" class=\"data row2 col0\" >service</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow2_col1\" class=\"data row2 col1\" >16134.1</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow2_col2\" class=\"data row2 col2\" >14583.3</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow2_col3\" class=\"data row2 col3\" >933</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow2_col4\" class=\"data row2 col4\" >0.99%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >6</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow3_col0\" class=\"data row3 col0\" >logistic</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow3_col1\" class=\"data row3 col1\" >15996.2</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow3_col2\" class=\"data row3 col2\" >14000</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow3_col3\" class=\"data row3 col3\" >1350</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow3_col4\" class=\"data row3 col4\" >1.43%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >1</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow4_col0\" class=\"data row4 col0\" >computer</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow4_col1\" class=\"data row4 col1\" >15752.1</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow4_col2\" class=\"data row4 col2\" >14000</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow4_col3\" class=\"data row4 col3\" >65762</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow4_col4\" class=\"data row4 col4\" >69.72%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow5_col0\" class=\"data row5 col0\" >gov</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow5_col1\" class=\"data row5 col1\" >15275.6</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow5_col2\" class=\"data row5 col2\" >12500</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow5_col3\" class=\"data row5 col3\" >1954</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow5_col4\" class=\"data row5 col4\" >2.07%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >10</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow6_col0\" class=\"data row6 col0\" >trade</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow6_col1\" class=\"data row6 col1\" >15062.7</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow6_col2\" class=\"data row6 col2\" >12500</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow6_col3\" class=\"data row6 col3\" >7199</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow6_col4\" class=\"data row6 col4\" >7.63%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow7_col0\" class=\"data row7 col0\" >medical</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow7_col1\" class=\"data row7 col1\" >14738.3</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow7_col2\" class=\"data row7 col2\" >13000</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow7_col3\" class=\"data row7 col3\" >2386</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow7_col4\" class=\"data row7 col4\" >2.53%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow8_col0\" class=\"data row8 col0\" >realestate</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow8_col1\" class=\"data row8 col1\" >14676.8</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow8_col2\" class=\"data row8 col2\" >12500</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow8_col3\" class=\"data row8 col3\" >1710</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow8_col4\" class=\"data row8 col4\" >1.81%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >0</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow9_col0\" class=\"data row9 col0\" >ads</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow9_col1\" class=\"data row9 col1\" >14406.6</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow9_col2\" class=\"data row9 col2\" >12500</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow9_col3\" class=\"data row9 col3\" >1815</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow9_col4\" class=\"data row9 col4\" >1.92%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0941e5c2_5830_11e9_a12d_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >3</th> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow10_col0\" class=\"data row10 col0\" >energy</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow10_col1\" class=\"data row10 col1\" >14319.6</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow10_col2\" class=\"data row10 col2\" >12500</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow10_col3\" class=\"data row10 col3\" >1635</td> \n",
       "        <td id=\"T_0941e5c2_5830_11e9_a12d_701ce71031efrow10_col4\" class=\"data row10 col4\" >1.73%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22daddc9390>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats(data,'industry_')\n",
    "data_industry.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "金融业果然是工资最高的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "nbformat": 4,
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